Online service evaluations that consider inconsistent user evaluation criteria usually use a complete ranking of services as the evaluation result, instead of selecting the Top-k online service set that maximizes the satisfaction of the user group. Thus, it makes the evaluation results cannot satisfy the rationality and fairness requirement in the scenario of Top-k online service evaluation. This study proposes a Top-k online service evaluation method that maximizes the satisfaction of user group. Firstly, a metric of user group satisfaction is defined to measure the rationality of the selected k online services. Secondly, considering the inconsistency of user evaluation criteria and incomplete user preference information, the Borda rule is used to construct user-service matrix based on users' preference relationship for online services. Then, inspired by the theory of Monroe proportional representation, the Top-k online service evaluation problem is modeled as an optimization problem to find a set of online services that maximizes satisfaction of the user group. Finally, a greedy algorithm is designed to solve the optimization problem and the obtained set of online services is served as the result of Top-k services evaluation. The rationality and effectiveness of the method are verified by theoretical analysis and experiments study. Theoretical analysis shows that the proposed method satisfies the proportional representation and fairness required for Top-k online service evaluation. Meanwhile, experiments also show that the method can obtain the result close to the ideal upper bound of the user group satisfaction in the reasonable time, so that the user group can make right service choice decision. In addition, the method can also realize Top-k online service evaluation when users' preferences are incomplete.